The invention relates to an equipment health monitoring method, an equipment health monitoring system and an equipment.
In engines like e.g. turbo turbines for aircraft full authority digital engine control (FADEC) and Equipment Health Monitoring (EHM) systems as such are known.
A FADEC system controls the turbo engine primarily for a safe and economically optimized operation. Therefore, the FADEC system collects a large number of flight data, e.g., such as air density, throttle lever position, engine temperatures, engine pressures. This system generally collects data through many channels which are available to the operating crew, and/or is automatically available for the control of the turbo engine.
The purpose of an EHM system is different. One purpose of the EHM system is to help with the long-term scheduling of turbo engine maintenance, e.g., determining the required level of turbo engine maintenance after a certain threshold of operating hours. The data available for assessment is called Engine Health Monitoring data or EHM.
EHM assessment and prognostic methods and systems which allow an efficient management of engines in general (e.g., combustion engines) and in particular turbo engines are therefore important.
This EHM data may vary from system to system and engine to engine. Furthermore, the EHM data does not necessarily have to be available to the flight crew, as these are long-term assessments which may be carried out off-line. The EHM is not limited to aircraft turbo engines but can be used in other engines as well, where data is monitored.
Existing stochastic EHM assessment and prognostic methods have typically overcome the EHM variability through the understanding that a probability distribution can be defined over the differences between measured EHM data and assumed or correlated EHM noiseless data. Using this methodology, stochastic methods produce an estimate of the most likely remaining useful life.
Non-stochastic EHM assessment methods can be applied to broader categories of EHM data (see Martinez, A., Sánchez, L., Couso, I. Engine Health Monitoring for engine fleets using fuzzy radviz. 2013 IEEE International Conference on Fuzzy Systems. Hyderabad, India. DOI 10.1109/FUZZ-IEEE.2013.6622420)
State-of-the art methods exist that filter EHM data, perform a soft quantization of the filtered EHM data, and transform sequences of quantized EHM data into a fixed-length fuzzy signature of the engine. A rule-based classifier maps these fuzzy signatures to one of the possible conditions of the engine (Good, Good to Normal, Normal, Normal to High and High deterioration). The result is an assessment of the engine associated to a confidence interval that bounds the worst-case accuracy of this diagnostic with a high probability.
Stochastic and non-stochastic EHM prognostic methods exist that predict the Remaining Useful Life (RUL) of an engine. The RUL of an engine is the expected number of safe operating hours or cycles before a given level of an engine, in particular a turbo engine maintenance is required. Existing non-stochastic methods of RUL prognosis through EHM data define the RUL as the initial release life, minus the life that has been consumed. A consumption factor is developed that depends on the degree of use that weights each cycle flown as a number of hours or cycles of remaining life used. Methods exist that apply rule-based classifiers to map the fuzzy signatures defined in Martinez, A., Sánchez, L., Couso, I. Engine Health Monitoring for engine fleets using fuzzy radviz. 2013 IEEE International Conference on Fuzzy Systems. Hyderabad, India. DOI 10.1109/FUZZ-IEEE.2013.6622420, to consumption factors and output a pessimistic bound of the RU (see Martinez, A., Sánchez, L., Couso, I. Aeroengine prognosis through genetic distal learning applied to uncertain Engine Health Monitoring data. 2014 IEEE International Conference on Fuzzy Systems. Beijing, China. DOI 10.1109/FUZZ-IEEE.2014.6891678).